Using dispersed computation to change dispersal characteristics

ABSTRACT

A method begins by determining an encoding modification for a set of encoded data slices. The method continues by determining a plurality of tasks for executing the encoding modification. The method continues by dividing a first task of the plurality of tasks into one or more partial tasks based on the first task and assigning a first partial task of the one or more partial tasks to a first storage unit. The method continues by dividing a second task of the plurality of tasks into a plurality of partial tasks based on the second task and assigning the plurality of partial tasks to a set of storage units. The method continues by executing, by the first storage unit and at least some storage units of the set of storage units, the first partial task and the plurality of partial tasks, respectively, to produce a modified set of encoded data slices.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. § 120 as a continuation-in-part of U.S. Utility applicationSer. No. 14/102,987, entitled “UPDATING SHARED GROUP INFORMATION IN ADISPERSED STORAGE NETWORK”, filed Dec. 11, 2013, which claims prioritypursuant to 35 U.S.C. § 119(e) to the following U.S. Provisional PatentApplication No. 61/760,962, entitled “MANAGING A DISPERSED STORAGENETWORK POWER CONSUMPTION”, filed Feb. 5, 2013, both of which are herebyincorporated herein by reference in their entirety and made part of thepresent U.S. Utility Patent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of an embodiment of a distributedcomputing system in accordance with the present invention;

FIG. 10 is a logic diagram of an example of a method of modifyingstorage of data in accordance with the present invention; and

FIG. 11 is a logic diagram of an example of a method of changingdispersal characteristics in a dispersed storage network (DSN) inaccordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data (e.g., data 40) on behalf of computing device 14. Withthe use of dispersed storage error encoding and decoding, the DSN 10 istolerant of a significant number of storage unit failures (the number offailures is based on parameters of the dispersed storage error encodingfunction) without loss of data and without the need for a redundant orbackup copies of the data. Further, the DSN 10 stores data for anindefinite period of time without data loss and in a secure manner(e.g., the system is very resistant to unauthorized attempts ataccessing the data).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSTN memory 22for a user device, a group of devices, or for public access andestablishes per vault dispersed storage (DS) error encoding parametersfor a vault. The managing unit 18 facilitates storage of DS errorencoding parameters for each vault by updating registry information ofthe DSN 10, where the registry information may be stored in the DSNmemory 22, a computing device 12-16, the managing unit 18, and/or theintegrity processing unit 20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSTN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of an embodiment of a distributedcomputing system that includes a computing device 96 and the at leasttwo distributed storage and task (DST) unit sets 88. The computingdevice may be implemented utilizing one or more of the computing device12-16 of FIG. 1, the storage unit 36 of FIG. 1, a DS unit, a storageserver, a distributed computing server, a user device, a DS processingunit, and a DST unit 88 of the DST unit set 82. The DST unit 88 may beimplemented by utilizing a storage unit 36 and DS client module of FIG.1.

The system functions to modify storage of data in a first DST unit set82 of the at least two DST unit sets 82 by utilizing a distributedcomputing storage modification process. The computing device 96 issuesslice access requests 90 to the first DST unit set 82 and receives sliceaccess responses 92. The computing device 96 identifies a data objectstored as a plurality of sets of encoded data slices for the storagemodification process based on the received slice access responses 92.For example, the computing device determines a measured reliabilitylevel based on the received slice access responses 92 and initiates thestorage modification process to improve reliability when the measuredreliability level compares unfavorably to a reliability level goal.

The computing device 96 determines one or more tasks of the storagemodification process (e.g., retrieve slices, decode slices, re-encodeslices, store slices). The determining may be based on one or more of alookup, receiving a task list, generating the task list based on thereceived slice access responses. The computing device 96 partitions eachof the one or more tasks to produce one or more storage modificationpartial tasks 94. The computing device 96 assigns each of the one ormore storage modification partial tasks 94 to one or more DST units 88of another DST unit set 82 of the two or more DST unit sets 82.Alternatively, the first DST unit set 82 includes at least one of theassigned one or more DST units 88. The computing device 96 outputs thestorage modification partial tasks 94 to the assigned one or more DSTunits 88.

The assigned one or more DST units 88 execute the storage modificationpartial tasks 94 to produce storage modification partial results 98. Thestorage modification partial results 98 include one or more of a slicename of a slice to be generated, the retrieved slice, a newly generatedslice, an error indicator, a set of modified slices, a set of newlygenerated slices, a data segment, and an indicator that the modifiedslices have been stored to retire the storage modification process. Theassigned one or more DST units 88 may issue slice access requests 90 tothe first DST unit set 82 and receive slice access responses 92 from thefirst DST unit set 82. The computing device 96 facilitates completion ofthe one or more tasks of the storage modification process utilizing thestorage modification partial results 98. For example, the computingdevice 96 stores newly generated slices. As another example, thecomputing device 96 updates a storage location list.

FIG. 10 is a logic diagram illustrating an example of modifying storageof data. The method begins at step 100 where a processing module (e.g.,of a computing device) identifies a data object stored in a dispersedstorage (DS) unit set for a storage modification process. Theidentifying may be based on one or more of a measured reliability level,a goal reliability level, an actual storage efficiency level, and a goalstorage efficiency level. The storage modification process may beinvoked when more or less reliability is desired and/or more or lessstorage efficiency is desired. The method continues at step 102 wherethe processing module determines one or more tasks of the storagemodification process. The determining includes identifying a new set ofstorage parameters and identifying the tasks to utilize the new set ofstorage parameters. For example, the processing module identifies thenew set of storage parameters where a new pillar width is less than aprevious pillar width when more storage efficiency is desired.

The method continues at step 104 where the processing module identifiesdistributed storage and task (DST) execution units to support the one ormore tasks of the storage modification process. The identifying may bebased on one or more of DST execution unit availability, an errormessage, and a DST execution unit encoding capability level. The methodcontinues with steps 106 and 108, where for each task of the one or moretasks, the processing module partitions the task to produce one or morepartial tasks and for each identified DST execution unit, the processingmodule assigns one or more partial tasks.

The method continues at step 110 where the processing module receivesone or more storage modification partial results. The method continuesat step 112 where the processing module facilitates completion of theone or more tasks of the storage modification process utilizing the oneor more storage modification partial results. For example, theprocessing module stores new slices. As another example, the processingmodule updates a storage location table. As yet another example, theprocessing module issues a command to a DST execution unit to store anewly generated slice.

FIG. 11 is a logic diagram illustrating a method of changing dispersalcharacteristics in a dispersed storage network (DSN). The method beginswith step 120, where a computing device of the DSN determines anencoding modification for a set of encoded data slices. Note to createthe set of encoded data slices, a data segment of data is dispersedstorage error encoded into the set of encoded data slices based ondispersed storage error encoding parameters. Note the encodingmodification includes altering one or more parameters of the dispersedstorage error encoding parameters. For example, the encodingmodification for the set of encoded data slices may include one or moreof deleting an encoded data slice(s), adding an encoded data slice(s),re-encoding the data segment using a new decode threshold, changingcoefficients (e.g., a-o of FIG. 5) of an encoding matrix, and changingan encoding function (e.g., from Reed Solomon to Cauchy-Reed Solomon,etc.).

The determining the encoding modification may be based on determinationfactors, which include one or more of greater reliability, modifying astorage location, responding to new hardware, and changing a storagetier. For example, the determination factors may include changingstorage of a set of encoded data slices from a cache (tier 1) to a harddrive (tier 2). As another example, determination factors may includechanging storage from a magnetic tape (tier 3) to double-parity RAID(tier 1). As yet another example, the determination factors may includechanging a physical address of an encoded data slice(s). As yet still afurther example, the determination factors may include a new hard drivebeing added to a storage unit and a storage unit being added to the setof storage units.

The method continues with step 122, where the computing devicedetermines a plurality of tasks for executing the encoding modification.As one example, when the encoding modification is to add an encoded dataslice a first task may be obtaining a data matrix and a second task maybe updating revision levels for the set of encoded data slices. Theplurality of tasks may also include a plurality of intermediate tasks(e.g., between a start of execution of the first task and an end ofexecution of the second task). In this example, the first intermediatetask is creating a new row of an encoding matrix, a second intermediatetask is performing a matrix multiplication of the new row by the datamatrix to produce the encoded data slice and a third intermediate taskis storing the encoded data slice.

As another example of the plurality of intermediate tasks, when theencoding modification is to delete an encoded data slice, a first taskmay be identifying the encoded data slice and a second task may beupdating slice names for the set of encoded data slices. In thisexample, a first intermediate task may be sending a delete command to astorage unit of the set of storage units that is storing the identifiedencoded data slice.

As yet another example of the plurality of intermediate tasks, when theencoding modification is to change an encoding matrix (e.g., changingdue to a decode threshold change, changing due to different encodingfunction, etc.), the first task may be obtaining a data matrix and asecond task may be updating revision levels for the set of encoded dataslices. In this example, a first intermediate task may be creating a newencoding matrix, a second intermediate task may be performing a matrixmultiplication of the new encoding matrix by the data matrix to producea coded matrix, a third intermediate task may be transforming the codedmatrix to produce the modified set of encoded data slices and a fourthintermediate task may be storing the modified set of encoded dataslices. Each of the intermediate tasks may also include a plurality ofpartial intermediate tasks.

As an example of the plurality of partial intermediate tasks, the firstintermediate task includes a plurality of partial first intermediatetasks. A first partial first intermediate task may be obtaining a firstrow of the new encoding matrix, a second partial first intermediate taskmay be obtaining a second row of the new encoding matrix and a thirdpartial first intermediate task may be obtaining a third row of the newencoding matrix. As another example, the second intermediate taskincludes a plurality of partial second intermediate tasks. A firstpartial second intermediate task may be multiplying the first row of thenew encoding matrix with the data matrix to produce a first new encodeddata slice of the modified set of encoded data slices and a secondpartial second intermediate task may be multiplying the second row ofthe new encoding matrix with the data matrix to produce a second newencoded data slice of the modified set of encoded data slices.

The method continues with step 124, where the computing device divides afirst task of the plurality of tasks into one or more partial tasksbased on the first task. For example, when the first task is obtaining adata matrix, a first partial first task may be obtaining a first portionof a data matrix and a second partial first task may be obtaining asecond portion of the data matrix.

The method continues with step 126, where the computing device assigns afirst partial task of the one or more partial tasks to a first storageunit. The method continues with step 128, where the computing devicedivides a second task of the plurality of tasks into a plurality ofpartial tasks based on the second task. For example, a first partialsecond task may be updating a first slice name of a set of slice namesand a second partial second task may be updating a second slice name ofthe set of slice names.

The method continues with step 130, where the computing device assignsthe plurality of partial tasks to a set of storage units. For example,the first partial second task may be assigned to a first storage unit ofthe set of storage units and the second partial second task may beassigned to a second storage unit of the set of storage units.

The method continues with step 132, where the first storage unit and atleast some storage units of the set of storage units execute the firstpartial task and the plurality of partial tasks, respectively, toproduce a modified set of encoded data slices.

The respective storage units may then store or send to another storageunit for storage, an encoded data slice(s) of the modified set ofencoded data slices.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method comprises: determining, by a computingdevice, an encoding modification for a set of encoded data slices,wherein a data segment of data is dispersed storage error encoded intothe set of encoded data slices based on dispersed storage error encodingparameters; determining, by the computing device, a plurality of tasksfor executing the encoding modification, wherein the encodingmodification includes altering one or more parameters of the dispersedstorage error encoding parameters; dividing, by the computing device, afirst task of the plurality of tasks into one or more partial tasksbased on the first task; assigning, by the computing device, a firstpartial task of the one or more partial tasks to a first storage unit;dividing, by the computing device, a second task of the plurality oftasks into a plurality of partial tasks based on the second task;assigning, by the computing device, the plurality of partial tasks to aset of storage units; and executing, by the first storage unit and atleast some storage units of the set of storage units, the first partialtask and the plurality of partial tasks, respectively, to produce amodified set of encoded data slices.
 2. The method of claim 1, whereinthe encoding modification comprises one or more of: deleting one or moreencoded data slices; adding one or more encoded data slices; re-encodingthe data segment using a new decode threshold; changing coefficients ofan encoding matrix; and changing an encoding function.
 3. The method ofclaim 1, wherein determination factors for the determining the encodingmodification comprises: comparing a measured reliability to areliability threshold; modifying a storage location; responding to newhardware; and changing a storage tier.
 4. The method of claim 1, whereinwhen the encoding modification is to add an encoded data slice:determining, by the computing device, the first task is obtaining a datamatrix; determining, by the computing device: a first intermediate taskis creating a new row of an encoding matrix; a second intermediate taskis performing a matrix multiplication of the new row by the data matrixto produce the encoded data slice; and a third intermediate task isstoring the encoded data slice; and determining, by the computingdevice, the second task is updating slice names for the set of encodeddata slices.
 5. The method of claim 1, wherein when the encodingmodification is to delete an encoded data slice: determining, by thecomputing device, the first task is identifying the encoded data slice;determining, by the computing device: a first intermediate task issending a delete command to a storage unit of the set of storage unitsthat is storing the identified encoded data slice; and determining, bythe computing device, the second task is updating slice names for set ofencoded data slices.
 6. The method of claim 1, wherein when the encodingmodification is to change an encoding matrix: determining, by thecomputing device, the first task is obtaining a data matrix;determining, by the computing device: a first intermediate task iscreating a new encoding matrix; a second intermediate task is performinga matrix multiplication of the new encoding matrix by the data matrix toproduce a coded matrix; a third intermediate task is transforming thecoded matrix to produce the modified set of encoded data slices; and afourth intermediate task is storing the modified set of encoded dataslices; and determining, by the computing device, the second task isupdating slice names for the modified set of encoded data slices.
 7. Themethod of claim 6 further comprises: determining, by the computingdevice, the first intermediate task includes a plurality of partialfirst intermediate tasks, wherein the plurality of partial firstintermediate tasks include a first partial first intermediate task ofobtaining a first row of the new encoding matrix; and determining, bythe computing device, the second intermediate task includes a pluralityof partial second intermediate tasks, wherein the plurality of partialsecond intermediate tasks include a first partial second intermediatetask of multiplying the first row of the new encoding matrix with thedata matrix to produce a first new encoded data slice of the modifiedset of encoded data slices.
 8. The method of claim 7, wherein theplurality of partial first intermediate tasks includes a second partialfirst intermediate task of obtaining a second row of the new encodingmatrix; and the plurality of partial second intermediate tasks includesa second partial second intermediate task of multiplying the second rowof the new encoding matrix with the data matrix to produce a second newencoded data slice of the modified set of encoded data slices.
 9. Anon-transitory computer readable storage device comprises: a firstmemory section for storing operational instructions that when executedby a computing device of a dispersed storage network (DSN), cause thecomputing device to: determine an encoding modification for a set ofencoded data slices, wherein a data segment of data is dispersed storageerror encoded into the set of encoded data slices based on dispersedstorage error encoding parameters; a second memory section for storingoperational instructions, that when executed by the computing device,cause the computing device to: determine a plurality of tasks forexecuting the encoding modification, wherein the encoding modificationincludes altering one or more parameters of the dispersed storage errorencoding parameters; a third memory section for storing operationalinstructions, that when executed by the computing device, cause thecomputing device to: divide a first task of the plurality of tasks intoone or more partial tasks based on the first task; assign a firstpartial task of the one or more partial tasks to a first storage unit;divide a second task of the plurality of tasks into a plurality ofpartial tasks based on the second task; assign the plurality of partialtasks to a set of storage units; and a fourth memory section for storingoperational instructions, that when executed by the first storage unitand at least some storage units of the set of storage units, cause thefirst storage unit and at least some storage units of the set of storageunits to: execute the first partial task and the plurality of partialtasks, respectively, to produce a modified set of encoded data slices.10. The non-transitory computer readable storage device of claim 9,wherein the encoding modification comprises one or more of: deleting oneor more encoded data slices; adding one or more encoded data slices;re-encoding the data segment using a new decode threshold; changingcoefficients of an encoding matrix; and changing an encoding function.11. The non-transitory computer readable storage device of claim 9,wherein determination factors for the determining the encodingmodification comprises: comparing a measured reliability to areliability threshold; modifying a storage location; responding to newhardware; and changing a storage tier.
 12. The non-transitory computerreadable storage device of claim 9, wherein the second memory sectionstores further operational instructions that, when the encodingmodification is to add an encoded data slice, cause the computing deviceto: determine the first task is obtaining a data matrix; determine: afirst intermediate task is creating a new row of an encoding matrix; asecond intermediate task is performing a matrix multiplication of thenew row by the data matrix to produce the encoded data slice; and athird intermediate task is storing the encoded data slice; and determinethe second task is updating slice names for the set of encoded dataslices.
 13. The non-transitory computer readable storage device of claim9, wherein the second memory section stores further operationalinstructions that, when the encoding modification is to delete anencoded data slice, cause the computing device to: determine the firsttask is identifying the encoded data slice; determine a firstintermediate task is sending a delete command to a storage unit of theset of storage units that is storing the identified encoded data slice;and determine the second task is updating slice names for set of encodeddata slices.
 14. The non-transitory computer readable storage device ofclaim 9, wherein the second memory section stores further operationalinstructions that, when the encoding modification is to change anencoding matrix, cause the computing device to: determine the first taskis obtaining a data matrix; determine: a first intermediate task iscreating a new encoding matrix; a second intermediate task is performinga matrix multiplication of the new encoding matrix by the data matrix toproduce a coded matrix; a third intermediate task is transforming thecoded matrix to produce the modified set of encoded data slices; and afourth intermediate task is storing the modified set of encoded dataslices; and determine the second task is updating slice names for themodified set of encoded data slices.
 15. The non-transitory computerreadable storage device of claim 14 further comprises: a fifth memorysection for storing operational instructions, that when executed by thecomputing device, cause the computing device to: determine the firstintermediate task includes a plurality of partial first intermediatetasks, wherein the plurality of partial first intermediate tasks includea first partial first intermediate task of obtaining a first row of thenew encoding matrix; and determine the second intermediate task includesa plurality of partial second intermediate tasks, wherein the pluralityof partial second intermediate tasks include a first partial secondintermediate task of multiplying the first row of the new encodingmatrix with the data matrix to produce a first new encoded data slice ofthe modified set of encoded data slices.
 16. The non-transitory computerreadable storage device of claim 15, wherein the fifth memory sectionstores further operational instructions that when executed by thecomputing device, cause the computing device to: determine the pluralityof partial first intermediate tasks includes a second partial firstintermediate task of obtaining a second row of the new encoding matrix;and determine the plurality of partial second intermediate tasksincludes a second partial second intermediate task of multiplying thesecond row of the new encoding matrix with the data matrix to produce asecond new encoded data slice of the modified set of encoded dataslices.